July 24, 2011

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Proactive Risk Management – New KPIs for a Dodd-Frank World

by Ravi Kalakota

The financial crisis of 2007–2011 is driving widespread changes in the U.S regulatory system. Dodd-Frank Act addresses “too big to fail” problem by tightening capital requirements and supervision of large financial firms and hedge funds. It also creates an “orderly liquidation authority” so the government can wind down a failing institution without market chaos.

Financial institutions will be spending billions to strengthen, streamline and automate their recordkeeping, risk management KPIs and dashboard systems. The implications on Data Retention and Archiving, Disaster Recovery and Continuity Planning have been well covered. But leveraging Business Analytics to proactively and reactively manage/monitor risk and compliance is an emerging frontier.

We believe that Business Analytics and real-time data management are poised to play a huge role in regulating the next generation of risk and compliance management in Financial Services industry (FSI). in this posting, we are going to examine the strategic and structural challenges, the dashboards and KPIs of interest that provide feedback, and what an effective execution roadmap needs to be for every organization.

Need for risk management to be an integral part of the business strategy, not an independent activity focused on loss

4) Industry consolidation and convergence

Competing on price, customer service, marketing, and efficiency

Operating in diverse, fragmented markets with different regulations and cultures

Need to integrate multiple business models into a unified strategic focus

5) Changing population demographics

Increased competition to retain and attract assets of a risk-averse aging population that has just experienced a historic drop in asset value

6) Complexity of financial instruments and markets

High transparency, effective knowledge transfer, and collaboration from front to back office

Bottomline — Large financial services firms are already struggling to manage their petabyte data stores in a way that satisfies existing regulatory requirements. With the changes outlined about, the data management problem becomes quite a large undertaking with reverberations across the application and infrastructure landscape.

Structural Challenges

The Dodd-Frank Act mandates a number of significant changes that include improving regulatory reporting capabilities. Changes will need to be executed in a coordinated fashion across number of business units. Will require a well managed information flow across different business units, selling and execution platforms.

The factors that inhibit change include:

Large institutions are a collection of widely diverse business lines:

Each with their own customers, business models, processes, risks, skills, and cultures

Each with their own legacy technologies

Each with their own data (silos of information)

Each producing significant amounts of data daily

Operating globally in a highly regulated environment with unique fiduciary relationship with customers

BI projects are usually technology driven and focus on the data and creating the perfect DW

Using BI is the responsibility of business units, not technology, and has historically been created and delivered under the direct control of the business unit

Bottomline – While the external pressure is building, firms also have to deal with internal structural and overhead issues. They will need a focused program management structure to manage the transition.

Dodd Frank KPI’s and Dashboards

Analytics are seen in all aspects of the enterprise – Front, Middle and Back-office.

Dodd-Frank also has broad implications for hedge funds. It requires all large hedge fund advisers to register with the SEC. Also the new rules on derivatives trading have an additional monitoring and reporting implications for many hedge funds.

As shown in the figure below, the Act leaves many specifics up to the regulators (FDIC, SEC, Federal Reserve, Treasury and numerous other oversight agencies). Considerable uncertainty remains about the exact form of the new rules and what needs to be reported. The main tradeoff is between the government’s desire to learn more about what is going on, both to assess systemic risk and to protect taxpayers, and the compliance costs this imposes on firms and shareholders.

Bottomline — Support for new regulations will force firms to not only acquire more data for enterprise risk management but also provide increased transparency into their data. Firms will spend millions annually on technology and people to facilitate the process of bringing in and integrating reference data.

Call to Action – Creating an Effective Risk Analytics and KPI Roadmap

So, what’s next? What are the steps in meeting the demands of the regulators and better manage risk? What needs to be done in a phased manner? Here are some steps to follow as you go about strengthening your current and ongoing readiness.

Start by forming a working group to improve coordination among various segments of the organization

Clarify issues and formulate strategies, and develop action plans

Perform a gap analysis by determining areas affected by legislation; determine processes in place and those needed

Avoid the pitfall of scoping the program too widely. Taking an overzealous view of information governance as it pertains to every bit of data across an entire organization can be dangerous to keeping a program on track.

2) Leverage existing data and business processes

With Data Appliances, institutions can quickly deploy a BI solution across multiple data sources without waiting for the “perfect” DW to be created

Rapid deployment within existing processes ensures high user adaption rates, quick return on investment, and all the benefits of an enterprise-quality BI application

Customer engagement, attrition, cross-sell, and up-sell opportunities can be identified with data mining and segmentation analysis of the granular risk data

7) Improve returns with more transparent financial reporting

Balance sheet reporting that links balances, rates, interest income, interest expense, and funds transfer pricing at the business and department level creates an understanding of the balance sheet value across the enterprise and strategic control of the value by management

Transparency in funds transfer pricing calculations by being able to drill down to individual instruments is an important part of the business

Summary

Regulatory scrutiny is increasing around the world, a trend that will likely continue. The new risk and compliance management models requires a mix of accountability, transparency, integrity, protection, compliance, availability, retention and disposition. This is a substantial multi-year transformation effort.

Passage of the Dodd–Frank Wall Street Reform and Consumer Protection Act = More government oversight and increased transparency

Proliferation of counterparties due to the rise in Latin America, South America, and Asia markets

It won’t be easy for leadership to change existing financial institutions. To go from calcified corporate cultures that results in business units hoarding their own information in silos into one that embraces risk management, transparency, and governance as a collective cause is not going to be easy or painless.

Other Sources

We are simply in the first inning of a long transformation cycle for Financial Institutions. If you are looking for additional insights around business analytics for regulatory oversight, contact us.

Alvarez & Marsal has developed a dedicated Dodd-Frank practice based on the work we have done at Lehman Brothers (the only real-world case study of Complex Financial Institution Orderly Liquidation and “Too Big to Fail”). Creating “Living Wills” is another area of A&M expertise.

Link your risk evaluation to every aspect of operations Take advantage of business treasury tools that function for financial risk management, regulatory compliance, and cashflow and liquidity monitoring. Track and handle your cash flow and liquidity

Defining Business Analytics

What is Business Analytics? Business Analytics is the intersection of business and technology, offering new opportunities for a competitive advantage. Business analytics unlocks the predictive potential of data analysis to improve financial performance, strategic management, and operational efficiency.

What is BI? BI is the "computer-based techniques used in spotting, digging-out, and analyzing 'hard' business data, such as sales revenue by products or departments or associated costs and incomes. Objectives of BI implementations include (1) understanding of a firm's internal and external strengths and weaknesses, (2) understanding of the relationship between different data for better decision making, (3) detection of opportunities for innovation, and (4) cost reduction and optimal deployment of resources." (Business Dictionary). Most widely used BI tool is Microsoft Excel.
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What is Big Data? Big data refer to data scenarios that grow so large (petabytes and more) that they become awkward to work with using traditional database management tools. The challenge stems from data volume + flow velocity + noise to signal conversion. Big data is spawning new tools that are mix of significant processing power, parallelism and statistical, machine learning, or pattern recognition techniques
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Corporate performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
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Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
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BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
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Data integration tools and architectures in support of BI continue to evolve. Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
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BI is about "sense and respond." Analytics is about "anticipate and shape" models.

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Business Analytics 3.0 blog is meant for decision makers and managers who are trying to make sense of the rapidly changing technology landscape and build next generation solutions. It is aimed at helping business decision makers navigate the "Raw Data -> Aggregate Data -> Intelligence -> Insight -> Decisions" chain.